it would be much better if we had an API that was trying to help the user instead of trying to predict the next word of text from the internet.
“I’m from OpenAI, and I’m here to help you”.
Seriously, it’s not obvious that you’re going to do anything but make things worse by trying to make the thing “try to help”. I don’t even see how you could define or encode anything meaningfully related to “helping” at this stage anyway.
As for the bottom line, I can imagine myself buying access to the best possible text predictor, but I can’t imagine myself buying access to something that had been muddied with whatever idea of “helpfulness” you might have. I just don’t want you or your code making that sort of decision for me, thanks.
(Upvoted, because jbash is a good commenter and it’s a pretty reasonable question for someone unacquainted with Paul’s work.)
Hey jbash. So, while you’re quite right in the short term that in general the ‘helpful’ bots we build are irritating and inflexible (e.g. Microsoft’s Clippy), the main point of a lot of Paul’s AI research is to figure out how to define helpfulness in such a way that an ML system can successfully be trained to do it – the hard problem of defining ‘helpfulness’, not the short term version of “did a couple of users say it was helpful and did the boss say ship it”. He’s written about it in this post, and given a big-picture motivation for it here.
It’s abstract and philosophically hard and it’s quite plausibly will just not work out, but I do think Paul is explicitly attempting to solve the hard version of the problem with the full knowledge of what you said.
I think that “imitate a human who is trying to be helpful” is better than “imitate a human who is writing an article on the internet,” even though it’s hard to define “helpful.” I agree that’s not completely obvious for a bunch of reasons.
(GPT-3 is better if your goal is in fact to predict text that people write on the internet, but that’s a minority of API applications.)
“I’m from OpenAI, and I’m here to help you”.
Seriously, it’s not obvious that you’re going to do anything but make things worse by trying to make the thing “try to help”. I don’t even see how you could define or encode anything meaningfully related to “helping” at this stage anyway.
As for the bottom line, I can imagine myself buying access to the best possible text predictor, but I can’t imagine myself buying access to something that had been muddied with whatever idea of “helpfulness” you might have. I just don’t want you or your code making that sort of decision for me, thanks.
(Upvoted, because jbash is a good commenter and it’s a pretty reasonable question for someone unacquainted with Paul’s work.)
Hey jbash. So, while you’re quite right in the short term that in general the ‘helpful’ bots we build are irritating and inflexible (e.g. Microsoft’s Clippy), the main point of a lot of Paul’s AI research is to figure out how to define helpfulness in such a way that an ML system can successfully be trained to do it – the hard problem of defining ‘helpfulness’, not the short term version of “did a couple of users say it was helpful and did the boss say ship it”. He’s written about it in this post, and given a big-picture motivation for it here.
It’s abstract and philosophically hard and it’s quite plausibly will just not work out, but I do think Paul is explicitly attempting to solve the hard version of the problem with the full knowledge of what you said.
I think that “imitate a human who is trying to be helpful” is better than “imitate a human who is writing an article on the internet,” even though it’s hard to define “helpful.” I agree that’s not completely obvious for a bunch of reasons.
(GPT-3 is better if your goal is in fact to predict text that people write on the internet, but that’s a minority of API applications.)